Podcasts about uri alon

Uri Alon is a pioneering farmer and lover of the land of Israel. Like so many in the South, on 10/7/23 he suffered many personal losses. He also is unable currently to welcome guests to The Salad Trail, an oasis of beauty and Israeli ingenuity in the depths of the desert. The farm requires support - both financial and physical - during this difficult time. Please consider donating to Uri's fund by purchasing future tickets, dedicating rows of produce or other options listed on his website.

This podcast is a commentary and does not contain any copyrighted material of the reference source. We strongly recommend accessing/buying the reference source at the same time. ■Reference Source https://www.ted.com/talks/uri_alon_why_science_demands_a_leap_into_the_unknown ■Post on this topic (You can get FREE learning materials!) https://englist.me/115-academic-words-reference-from-uri-alon-why-science-demands-a-leap-into-the-unknown-ted-talk/ ■Youtube Video https://youtu.be/5ywUArzN9LQ (All Words) https://youtu.be/6JtFrf3RvOM (Advanced Words) https://youtu.be/XbdLIxVf47c (Quick Look) ■Top Page for Further Materials https://englist.me/ ■SNS (Please follow!)

This podcast is a commentary and does not contain any copyrighted material of the reference source. We strongly recommend accessing/buying the reference source at the same time. ■Reference Source https://www.ted.com/talks/uri_alon_a_covid_19_exit_strategy_to_end_lockdown_and_reopen_the_economy ■Post on this topic (You can get FREE learning materials!) https://englist.me/121-academic-words-reference-from-uri-alon-a-covid-19-exit-strategy-to-end-lockdown-and-reopen-the-economy--ted-talk/ ■Youtube Video https://youtu.be/-N7tKPqXhjA (All Words) https://youtu.be/dAs2__6ylso (Advanced Words) https://youtu.be/GlVS9PR0qso (Quick Look) ■Top Page for Further Materials https://englist.me/ ■SNS (Please follow!)

What are network motifs, and how and why do they matter to biological networks? On this episode, we talk with Uri Alon, systems biologist at the Weizmann Institute of Science, about biological networks. In the early 2000s, Uri discovered some of the fundamental characteristics of these networks and, since then, has worked to understand networks across different levels of biological organization. His work shows that, from genes to whole organisms, networks are filled with repeating patterns of connections known as network motifs, such as feedback and feedforward loops. We talk about how the motifs arise and what they mean for the performance and evolution of the systems in which they're embedded. Moving farther afield, we also talk about how scientists can productively move into new areas, and how Uri teaches early-stage scientists to leap confidently into the unknown. And a bonus: Uri sings and plays guitar for us! Cover art: Keating Shahmehri

Language models (LMs) compute the probability of a text by sequentially computing a representation of an already-seen context and using this representation to predict the next word. Currently, most LMs calculate these representations through a neural network consuming the immediate previous context. However recently, retrieval-augmented LMs have shown to improve over standard neural LMs, by accessing information retrieved from a large datastore, in addition to their standard, parametric, next-word prediction. In this paper, we set out to understand why retrieval-augmented language models, and specifically why k -nearest neighbor language models ( k NN-LMs) perform better than standard parametric LMs, even when the k -nearest neighbor component retrieves examples from the same training set that the LM was originally trained on. 2023: Frank F. Xu, Uri Alon, Graham Neubig https://arxiv.org/pdf/2301.02828v1.pdf

Uri Alon, a professor at the Weizmann Institute of Science in Israel, is best known for his contributions to systems biology. But Uri is also famous for his very joyful and playful attitude to science, which is memorable for anyone who's ever heard him speak (or sing). Uri's research is exceptionally broad in terms of the fields he covers, which is one reason why he is one of today's most cited researchers. We talked with Uri about a wide range of topics: about improvisation in science, about how to get unstuck, about how presentations can be creative and a chance to learn, and about how science needs all kinds of personalities to make progress. Uri discussed how to enter a new field, learn the field-specific language, and bring a new angle to it – by going into the ‘cloud' and tackling the unknown. In thinking about how to train students to be creative, Uri talked about how we each have an internal tuning fork, which aligns with certain types of scientific problems that match our personality. For more information on Night Science, visit www.night-science.org .

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Book Review: Design Principles of Biological Circuits, published by johnswentworth on the LessWrong. I remember seeing a talk by a synthetic biologist, almost a decade ago. The biologist used a genetic algorithm to evolve an electronic circuit, something like this: (source) He then printed out the evolved circuit, brought it to his colleague in the electrical engineering department, and asked the engineer to analyze the circuit and figure out what it did. “I refuse to analyze this circuit,” the colleague replied, “because it was not designed to be understandable by humans.” He has a point - that circuit is a big, opaque mess. This, the biologist argued, is the root problem of biology: evolution builds things from random mutation, connecting things up without rhyme or reason, into one giant spaghetti tower. We can take it apart and look at all the pieces, we can simulate the whole thing and see what happens, but there's no reason to expect any deeper understanding. Organisms did not evolve to be understandable by humans. I used to agree with this position. I used to argue that there was no reason to expect human-intelligible structure inside biological organisms, or deep neural networks, or other systems not designed to be understandable. But over the next few years after that biologist's talk, I changed my mind, and one major reason for the change is Uri Alon's book An Introduction to Systems Biology: Design Principles of Biological Circuits. Alon's book is the ideal counterargument to the idea that organisms are inherently human-opaque: it directly demonstrates the human-understandable structures which comprise real biological systems. Right from the first page of the introduction: . one can, in fact, formulate general laws that apply to biological networks. Because it has evolved to perform functions, biological circuitry is far from random or haphazard. ... Although evolution works by random tinkering, it converges again and again onto a defined set of circuit elements that obey general design principles. The goal of this book is to highlight some of the design principles of biological systems... The main message is that biological systems contain an inherent simplicity. Although cells evolved to function and did not evolve to be comprehensible, simplifying principles make biological design understandable to us. It's hard to update one's gut-level instinct that biology is a giant mess of spaghetti without seeing the structure first hand, so the goal of this post is to present just enough of the book to provide some intuition that, just maybe, biology really is human-understandable. This review is prompted by the release of the book's second edition, just this past August, and that's the edition I'll follow through. I will focus specifically on the parts I find most relevant to the central message: biological systems are not opaque. I will omit the last three chapters entirely, since they have less of a gears-level focus and more of an evolutionary focus, although I will likely make an entire separate post on the last chapter (evolution of modularity). Chapters 1-4: Bacterial Transcription Networks and Motifs E-coli has about 4500 proteins, but most of those are chunked together into chemical pathways which work together to perform specific functions. Different pathways need to be expressed depending on the environment - for instance, e-coli won't express their lactose-metabolizing machinery unless the environment contains lots of lactose and not much glucose (which they like better). In order to activate/deactivate certain genes depending on environmental conditions, bacteria use transcription factors: proteins sensitive to specific conditions, which activate or repress transcription of genes. We can think of the transcription factor activity as the cell's interna...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Evolution of Modularity, published by johnswentworth on the AI Alignment Forum. Crossposted from the AI Alignment Forum. May contain more technical jargon than usual. This post is based on chapter 15 of Uri Alon's book An Introduction to Systems Biology: Design Principles of Biological Circuits. See the book for more details and citations; see here for a review of most of the rest of the book. Fun fact: biological systems are highly modular, at multiple different scales. This can be quantified and verified statistically, e.g. by mapping out protein networks and algorithmically partitioning them into parts, then comparing the connectivity of the parts. It can also be seen more qualitatively in everyday biological work: proteins have subunits which retain their function when fused to other proteins, receptor circuits can be swapped out to make bacteria follow different chemical gradients, manipulating specific genes can turn a fly's antennae into legs, organs perform specific functions, etc, etc. On the other hand, systems designed by genetic algorithms (aka simulated evolution) are decidedly not modular. This can also be quantified and verified statistically. Qualitatively, examining the outputs of genetic algorithms confirms the statistics: they're a mess. So: what is the difference between real-world biological evolution vs typical genetic algorithms, which leads one to produce modular designs and the other to produce non-modular designs? Kashtan & Alon tackle the problem by evolving logic circuits under various conditions. They confirm that simply optimizing the circuit to compute a particular function, with random inputs used for selection, results in highly non-modular circuits. However, they are able to obtain modular circuits using “modularly varying goals” (MVG). The idea is to change the reward function every so often (the authors switch it out every 20 generations). Of course, if we just use completely random reward functions, then evolution doesn't learn anything. Instead, we use “modularly varying” goal functions: we only swap one or two little pieces in the (modular) objective function. An example from the book: The upshot is that our different goal functions generally use similar sub-functions - suggesting that they share sub-goals for evolution to learn. Sure enough, circuits evolved using MVG have modular structure, reflecting the modular structure of the goals. (Interestingly, MVG also dramatically accelerates evolution - circuits reach a given performance level much faster under MVG than under a fixed goal, despite needing to change behavior every 20 generations. See either the book or the paper for more on that.) How realistic is MVG as a model for biological evolution? I haven't seen quantitative evidence, but qualitative evidence is easy to spot. MVG as a theory of biological modularity predicts that highly variable subgoals will result in modular structure, whereas static subgoals will result in a non-modular mess. Alon's book gives several examples: Chemotaxis: different bacteria need to pursue/avoid different chemicals, with different computational needs and different speed/energy trade-offs, in various combinations. The result is modularity: separate components for sensing, processing and motion. Animals need to breathe, eat, move, and reproduce. A new environment might have different food or require different motions, independent of respiration or reproduction - or vice versa. Since these requirements vary more-or-less independently in the environment, animals evolve modular systems to deal with them: digestive tract, lungs, etc. Ribosomes, as an anti-example: the functional requirements of a ribosome hardly vary at all, so they end up non-modular. They have pieces, but most pieces do not have an obvious distinct function. To sum it up: modul...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Evolution of Modularity, published by johnswentworth on the AI Alignment Forum. Write a Review This post is based on chapter 15 of Uri Alon's book An Introduction to Systems Biology: Design Principles of Biological Circuits. See the book for more details and citations; see here for a review of most of the rest of the book. Fun fact: biological systems are highly modular, at multiple different scales. This can be quantified and verified statistically, e.g. by mapping out protein networks and algorithmically partitioning them into parts, then comparing the connectivity of the parts. It can also be seen more qualitatively in everyday biological work: proteins have subunits which retain their function when fused to other proteins, receptor circuits can be swapped out to make bacteria follow different chemical gradients, manipulating specific genes can turn a fly's antennae into legs, organs perform specific functions, etc, etc. On the other hand, systems designed by genetic algorithms (aka simulated evolution) are decidedly not modular. This can also be quantified and verified statistically. Qualitatively, examining the outputs of genetic algorithms confirms the statistics: they're a mess. So: what is the difference between real-world biological evolution vs typical genetic algorithms, which leads one to produce modular designs and the other to produce non-modular designs? Kashtan & Alon tackle the problem by evolving logic circuits under various conditions. They confirm that simply optimizing the circuit to compute a particular function, with random inputs used for selection, results in highly non-modular circuits. However, they are able to obtain modular circuits using “modularly varying goals” (MVG). The idea is to change the reward function every so often (the authors switch it out every 20 generations). Of course, if we just use completely random reward functions, then evolution doesn't learn anything. Instead, we use “modularly varying” goal functions: we only swap one or two little pieces in the (modular) objective function. An example from the book: The upshot is that our different goal functions generally use similar sub-functions - suggesting that they share sub-goals for evolution to learn. Sure enough, circuits evolved using MVG have modular structure, reflecting the modular structure of the goals. (Interestingly, MVG also dramatically accelerates evolution - circuits reach a given performance level much faster under MVG than under a fixed goal, despite needing to change behavior every 20 generations. See either the book or the paper for more on that.) How realistic is MVG as a model for biological evolution? I haven't seen quantitative evidence, but qualitative evidence is easy to spot. MVG as a theory of biological modularity predicts that highly variable subgoals will result in modular structure, whereas static subgoals will result in a non-modular mess. Alon's book gives several examples: Chemotaxis: different bacteria need to pursue/avoid different chemicals, with different computational needs and different speed/energy trade-offs, in various combinations. The result is modularity: separate components for sensing, processing and motion. Animals need to breathe, eat, move, and reproduce. A new environment might have different food or require different motions, independent of respiration or reproduction - or vice versa. Since these requirements vary more-or-less independently in the environment, animals evolve modular systems to deal with them: digestive tract, lungs, etc. Ribosomes, as an anti-example: the functional requirements of a ribosome hardly vary at all, so they end up non-modular. They have pieces, but most pieces do not have an obvious distinct function. To sum it up: modularity in the system evolves to match modularity in the environment. Than...

전체 전체 강의 보기: https://www.ted.com/talks/uri_alon_a_covid_19_exit_strategy_to_end_lockdown_and_reopen_the_economy 어떻게 하면 코로나 바이러스의 2차 대유행을 촉발시키지 않고 일상으로 돌아갈 수 있을까요? 생물학자 유리 알론(Uri Alon)은 주목할만한 방안을 제시합니다. 4일 근무 후 10일의 격리 기간을 갖는 방안으로 이 주기를 반복함으로써 바이러스의 생물학적 취약점을 이용해서 잠재적으로는 재생산 감염 속도를 통제 가능한 수준으로 낮출 수 있습니다. 이미 몇몇 기업과 국가에서 시행 중에 있으며, 어떻게 이 방안이 책임감을 갖고 경제 재활성화의 핵심이 될 수 있는지, 이 방안에 대해 더 자세히 알려드립니다. (이 화상 대담은 TED 대표인 크리스 앤더슨(Chris Anderson)과 과학 큐레이터 데이비드 비엘로(David Biello)가 참여하였고, 2020년 5월 20일에 녹화된 영상입니다.)

Como podemos voltar ao trabalho sem provocar um segundo pico de transmissão do novo coronavírus? O biólogo Uri Alon compartilha uma estratégia instigante: quatro dias de trabalho seguidos por dez dias de isolamento total, um ciclo que exploraria um ponto fraco na biologia do vírus e reduziria possivelmente seu índice de reprodução a um nível controlável. Saiba mais sobre essa abordagem, que já foi adotada por empresas e países, e como ela pode ser a chave para reabrir a economia de maneira responsável. (Esta conversa virtual, organizada pelo presidente do TED Chris Anderson e pelo curador de ciências David Biello, foi gravada em 20 de maio de 2020.)

Comment pouvons-nous retourner au travail sans stimuler une deuxième vague d'infections du coronavirus ? Le biologiste Uri Alon partage une stratégie de réflexion : quatre jours au travail suivis de dix jours de confinement, un cycle qui exploiterait une faiblesse de la biologie du virus et pourrait potentiellement arrêter sa propagation. Apprenez-en plus sur cette approche - qui a déjà été adoptée par certaines entreprises et certains pays - et comment elle pourrait être une clé pour une réouverture économique responsable. (Cette conversation virtuelle, animée par le patron de TED Chris Anderson et le curateur pour les sciences David Biello a été enregistrée le 20 mai 2020.)

¿Cómo podemos regresar a nuestros lugares de trabajo sin provocar un rebrote de contagio del coronavirus? El biólogo Uri Alon sugiere una estrategia que mueve a la reflexión: 4 días de trabajo seguidos de 10 días de confinamiento. Se trata de un ciclo que busca explotar una debilidad biológica del virus y que podría interrumpir su tasa de contagio a niveles manejables. En esta charla, Alon presenta una propuesta que ya fue adoptada por algunos países y ciertas empresas, y explica cómo su aplicación podría ser clave en la reapertura de la economía. (Esta conversación virtual fue conducida por Chris Anderson, responsable de TED, y David Biello, curador científico. La grabación se realizó el 20 de mayo de 2020.)

How can we return to work without spurring a second surge of coronavirus infection? Biologist Uri Alon shares a thought-provoking strategy: four days at work followed by 10 days of lockdown, a cycle that would exploit a weakness in the virus's biology and potentially cut its reproductive rate to a manageable level. Learn more about this approach -- which has already been adopted by both companies and countries -- and how it could be a key to reopening the economy responsibly. (This virtual conversation, hosted by head of TED Chris Anderson and science curator David Biello, was recorded on May 20, 2020.)

How can we return to work without spurring a second surge of coronavirus infection? Biologist Uri Alon shares a thought-provoking strategy: four days at work followed by 10 days of lockdown, a cycle that would exploit a weakness in the virus's biology and potentially cut its reproductive rate to a manageable level. Learn more about this approach -- which has already been adopted by both companies and countries -- and how it could be a key to reopening the economy responsibly. (This virtual conversation, hosted by head of TED Chris Anderson and science curator David Biello, was recorded on May 20, 2020.)

How can we return to work without spurring a second surge of coronavirus infection? Biologist Uri Alon shares a thought-provoking strategy: four days at work followed by 10 days of lockdown, a cycle that would exploit a weakness in the virus's biology and potentially cut its reproductive rate to a manageable level. Learn more about this approach -- which has already been adopted by both companies and countries -- and how it could be a key to reopening the economy responsibly. (This virtual conversation, hosted by head of TED Chris Anderson and science curator David Biello, was recorded on May 20, 2020.)

How can we return to work without spurring a second surge of coronavirus infection? Biologist Uri Alon shares a thought-provoking strategy: four days at work followed by 10 days of lockdown, a cycle that would exploit a weakness in the virus's biology and potentially cut its reproductive rate to a manageable level. Learn more about this approach -- which has already been adopted by both companies and countries -- and how it could be a key to reopening the economy responsibly. (This virtual conversation, hosted by head of TED Chris Anderson and science curator David Biello, was recorded on May 20, 2020.)

クモに詳しいゲストをお呼びして、クモについて話しました。Show notes Albert-László Barabási … scale-free networkで知られるネットワーク科学研究者 Uri Alon … システム生物学の研究者。 Uri Alonによるシステム生物学の授業 (YouTube) … 以前から更新されているUri Alonによるシステム生物学の授業。授業の中でリラックスのために深呼吸を促すこともある。 システム生物学入門 (Amazon) … Uri Alonによるシステム生物学に関する入門教科書。 ネットワーク科学: ひと・もの・ことの関係性をデータから解き明かす新しいアプローチ (Amazon) … Barabásiらによるネットワーク科学の教科書 クラスタリング係数 The Extended Phenotype: The Long Reach of the Gene … Richard Dawkinsによる外部表現型に関する解説本 Bond et al. 2014 (Current Biology) … クモの巣の分類と分子系統進化に関する論文。クモの巣を作る能力は複数回獲得されたのか、それとも複数回失われたかについての議論。 Coddington et al. 2019 (PeerJ) … Bondグループによる最新の論文。 クモの巣について … クモの巣について。こちらを読むと理解が捗ります。 ハラフシグモ … ハラフシグモ科のクモ。腹部に体節の痕跡がある。かわいい。 ナゲナワグモ … 粘球のついた投げ縄を使って獲物を捕まえるクモの総称。日本にはムツトゲイセキグモ、マメイタイセキグモが生息している (谷川 1997)。ムツトゲイセキグモの生態については、新海らによる報告に詳しい (新海 and 新海 2002) オオヒメグモ … 人家やビルなどの人工物の近くによくいるクモ。円網ではなく、一見不規則な不規則網を張る。ゲノムが解読されている 、成体になるまでの期間が短い(2 ~ 3 ヶ月)、飼育がしやすいという理由から、発生の研究に使われており、JT生命誌研究館の小田広樹氏らによって発生生物学的なツールが精力的に整備されている。 (Oda and Akiyama-Oda 2020) ジョロウグモ … 秋に成体となる大型のクモ。おそらく最もポピュラーなクモの一つ。 CRISPR/Cas9 … ゲノム編集に使われるシステム。ep2などを参照のこと。 寄生バチ クモヒメバチ クモヒメバチに操作されるギンメッキゴミグモの全造網過程の動画 (YouTube) ゴミグモ … 円網の中心にゴミを貼り付けてカモフラージュすることからこの名前がある。 縦糸、横糸、枠糸 … 円網を構成する糸の呼称。ネバネバしているのは横糸のみ。 中島みゆき　糸 (Amazon) 幽★遊★白書 (Amazon) … 冨樫義博先生の名作。美しい魔闘家鈴木については幽遊白書を参考のこと(大した話ではない)。 バルーニング … クモが風に乗って遠くまで移動する方法。 maz … 生物の飛行と遊泳の研究を行う若手研究者。生物の飛行と遊泳に興味がある人はmazさんのtwitterを見よう！researchat.fmで手に入れている飛行と遊泳に関する情報はほぼこの人のtwitterがソース。 Cho et al. 2018 (PLoS Biology) … クモのバルーニングと風洞実験に関する論文 Watch a ‘ballooning’ spider take flight (YouTube) … 上記の論文に関連したクモのバルーニングの様子に関する動画 偽円網 … ボロアミグモ科の Fecenia が造る網は、円網種が造る円網と構造が類似しているためこう呼ばれる。徘徊性のクモであるが、円網と似た網を造るところが興味深い。 (Blackledge et al. 2012) クモの種類: World Spider Catalog によると、これを書いている時点で世界のクモの種類は 48422 種。 テラフォーマーズ (Amazon) ミノムシ キムラグモ … 九州、南西諸島に生息するハラフシグモ科のクモ。生きた化石と呼ばれ、移動能力が低く古くか存在しており、地理的な分化をしていることが知られている (Tanikawa 2013)。 ミズグモ … 世界で唯一水中で生活するクモ。現在はハグモ科に分類されている (World Spider Catalog)。 ダーウィンズ・バーク・スパイダー … 2010年にマダガスカルから報告された、世界で最もタフな糸を持つとされているクモ (Agnarsson, Kuntner, and Blackledge 2010)。川をまたぐように巣を張ることで知られている。造網行動について報告した論文にある、クモが糸を大量に出している動画は必見 (Gregorič et al. 2011)。 クモの目 … クモは単眼のみを持つ。配置と数は科によって異なり、種同定に使われる。 ザトウムシ ザトウムシとB染色体 闘蟋―中国のコオロギ文化 (Amazon) … “「闘蟋」―それはコオロギを闘わせ、ひと秋をかけて“虫王”を決める遊び。飼い主たちは、戦士の育成に持てる金と時間と知識のすべてを注ぎ、熱中のあまり家屋敷を失ったものは数知れず、一国を滅ぼした宰相さえいた。一二〇〇年の時を超え、男たちを魅了し続ける中国の闘うコオロギとは。 “ この紹介文を読んでこの本を読みたくならないものなどいないだろう！！！ 賈似道 … 南宋末期の政治家。闘蟋に入れ込み過ぎたことが知られている。その入れ込みから唐の時代からの闘蟋の歴史を書き記した百科事典、促織経を書き記す。しかしそのあと南宋はモンゴル帝国の前に屈する。 プッチ神父、DIO … みなさんご存知のジョジョの敵キャラ。「質問が悪かった…… 子供が遊びで話す『スタローンとジャン・クロード・バンダムはどっちが強い？』そのレベルでいいよ。」 とりたべるクモ … 沖縄ではオオジョロウグモが鳥を食べているところが目撃されることがある。クモに食べられた鳥について、これまでの報告をまとめた論文 (Walther 2016) もある。これを見るとオオジョロウグモはよく鳥を食べているようだ。ちなみにコウモリも食べるらしい (Nyffeler and Knörnschild 2013)。 ハエトリグモ … かわいい ハエトリグモハンドブック (Amazon) コガネグモ 加治木くも合戦 … 文禄・慶長の役の際、島津義弘が士気を高めるために始めたと言われる鹿児島のくも合戦。コガネグモを戦わせる。 浅野いにお メメクラゲ … 書く必要もないとおもうが、メメクラゲといえばねじ式、ねじ式といえばつげ義春。メメはxxの誤植。ポケモンのメノクラゲの由来でもある。 ハーバー・ボッシュ法 グアノ島法 大気を変える錬金術 (Amazon) ねこぢる大全 (Amazon) … ねこぢる読もう。 倫理とは何か 猫のアインジヒトの挑戦 (Amazon) Editorial notes 久々にクモの話が出来て楽しかったです (nakamura) こんなに奥深いクモの世界が広がっているとは… (soh) クモの行動遺伝学はおもしろく、ゲノム編集やシーケンシング技術が発展した今、これからの展開が楽しみです。(tadasu) nakamuraさん、ありがとう！クモすごい(coela)

Qu'est-ce que la créativité ? Etes-vous créatif ? Pouvez-vous le devenir ? Nous répondons à quelques unes de ces questions au fil de nos découvertes. Dans cet épisode vous serez surpris (nous l'espérons) par toutes les notions qui gravitent autour de la créativité. Vous apprendrez qu'au fil de l'âge nous sommes de moins en moins créatifs mais qu'il n'existe pas de fatalité et que l'on devient créatif plus qu'on ne l'apprend. Pour rappel, nous ne sommes pas des experts mais de simples curieux qui partageons ce qui nous a intéressé. Ainsi, si des éléments sont manquants ou erronés, n'hésitez pas à nous le faire savoir. Nous essaierons autant que possible de rectifier nos propos. Bonne écoute! Références: - R.E. Beaty et coll., « Robust Prediction of Individual Creative Ability from Brain Functional Connectivity », in Proceedings of the National Academy of Sciences, janv. 2018 - Eric J. Johnson and Daniel Goldstein, Do Defaults Save Lives?, Science 302 (2003) 1338:1339 - George Land and Beth Jarman, Breaking Point and Beyond. San Francisco: HarperBusiness, 1993 - Placebo can enhance creativity - Liron Rozenkrantz, Avraham E. Mayo, Tomer Ilan, Yuval Hart, Lior Noy , Uri Alon (2017) - Dreaming (Vol. 3, No. 2)- Deidre Barrett, PhD (1993) - Organizational Behavior and Human Decision Processes (Vol. 82, No. 1) - Paul Paulus (2000) - Creativity Research Journal (Vol. 14, No. 3.4) - Janetta Mitchell McCoy (2002) - Creativity Research Journal (Vol. 16, No. 2.3) - Karen Gasper (2004)

ABOUT THIS EPISODE The Salad Trail is one of the most underrated tourist attractions in Israel: a living example of the Zionist dream to "make the desert bloom." Uri Alon, its pioneering founder, embodies that dream, and has dedicated his life to this growth, and to sharing this beauty with others. -------------------- ABOUT THIS PODCAST Jews You Should Know introduces the broader community to interesting and inspiring Jewish men and women making a difference in our world. Some are already famous, some not yet so. But each is a Jew You Should Know. The host, Rabbi Ari Koretzky, is Executive Director of MEOR Maryland (www.meormd.org), a premier Jewish outreach and educational organization. MEOR operates nationally on twenty campuses and in Manhattan; visit the national website at www.meor.org. Please visit www.JewsYouShouldKnow.com, follow us on Twitter @JewsUShouldKnow or on Facebook. Have feedback for the show, or suggestions for future guests? E-mail us at JewsYouShouldKnow@gmail.com. Want to support this podcast? Visit Patreon.com/JewsYouShouldKnow. A small monthly contribution goes a long way!! A special thank you to Jacob Rupp of the Lift Your Legacy podcast for his invaluable production assistance.

Kidney stones in children have been on the rise for more than a decade, mostly due to hypercalciuria and hypocitraturia. Join us as Uri Alon, MD, Director of the Bone and Mineral Disorders Clinic at Children's Mercy Kansas City, discusses what is behind the increasing incidence of kidney stones, and medical and non-pharmacological interventions to prevent new stones and inhibit the growth of existing ones.

Kidney stones in children have been on the rise for more than a decade, mostly due to hypercalciuria and hypocitraturia. Join us as Uri Alon, MD, Director of the Bone and Mineral Disorders Clinic at Children’s Mercy Kansas City, discusses what is behind the increasing incidence of kidney stones, and medical and non-pharmacological interventions to prevent new stones and inhibit the growth of existing ones.